When applied to clean examples and their adversarial counterparts,​ logit pairing improves accuracy on adversarial examples over vanilla adversarial training; we also find that logit pairing on clean examples only is competitive with adversarial training in terms of accuracy on two datasets.

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https://​github.com/​VishaalMK/​VectorDefense VectorDefense:​ Vectorization as a Defense to Adversarial Examples

networks (GANs) and analyse its performance in simple tabular environments,​

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as well as OpenAI Gym. We empirically show that our algorithm leverages the

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flexibility and blackbox approach of deep learning models while providing a viable

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alternative to other state-of-the-art methods.

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https://​arxiv.org/​abs/​1805.12152v1 There Is No Free Lunch In Adversarial Robustness (But There Are Unexpected Benefits)

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​Robust models turn out to have interpretable gradients and feature representations that align unusually well with salient data characteristics. In fact, they yield striking feature interpolations that have thus far been possible to obtain only using generative models such as GANs.